Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations38688
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.5 MiB
Average record size in memory176.0 B

Variable types

Numeric17
Categorical4

Alerts

destination_bytes is highly overall correlated with dst_host_count and 3 other fieldsHigh correlation
destination_port_number is highly overall correlated with freq_flag and 1 other fieldsHigh correlation
dst_host_count is highly overall correlated with destination_bytes and 2 other fieldsHigh correlation
dst_host_serror_rate is highly overall correlated with dst_host_srv_serror_rateHigh correlation
dst_host_srv_count is highly overall correlated with destination_bytes and 3 other fieldsHigh correlation
dst_host_srv_serror_rate is highly overall correlated with dst_host_serror_rateHigh correlation
duration is highly overall correlated with source_bytesHigh correlation
freq_ashula is highly overall correlated with freq_protocolHigh correlation
freq_flag is highly overall correlated with destination_bytes and 6 other fieldsHigh correlation
freq_protocol is highly overall correlated with freq_ashula and 1 other fieldsHigh correlation
freq_service is highly overall correlated with labelHigh correlation
label is highly overall correlated with dst_host_srv_count and 2 other fieldsHigh correlation
source_bytes is highly overall correlated with destination_bytes and 1 other fieldsHigh correlation
source_port_number is highly overall correlated with destination_port_number and 1 other fieldsHigh correlation
same_srv_rate is highly imbalanced (75.3%)Imbalance
serror_rate is highly imbalanced (95.2%)Imbalance
label is highly imbalanced (60.9%)Imbalance
duration is highly skewed (γ1 = 196.2333222)Skewed
source_bytes is highly skewed (γ1 = 91.80270457)Skewed
destination_bytes is highly skewed (γ1 = 73.0083238)Skewed
duration has 11548 (29.8%) zerosZeros
source_bytes has 17698 (45.7%) zerosZeros
destination_bytes has 29413 (76.0%) zerosZeros
count has 33384 (86.3%) zerosZeros
srv_serror_rate has 27476 (71.0%) zerosZeros
dst_host_count has 27648 (71.5%) zerosZeros
dst_host_srv_count has 22340 (57.7%) zerosZeros
dst_host_same_src_port_rate has 35478 (91.7%) zerosZeros
dst_host_serror_rate has 37342 (96.5%) zerosZeros
dst_host_srv_serror_rate has 36220 (93.6%) zerosZeros
destination_port_number has 14104 (36.5%) zerosZeros

Reproduction

Analysis started2024-07-16 03:01:15.385526
Analysis finished2024-07-16 03:02:03.738790
Duration48.35 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

duration
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct22329
Distinct (%)57.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3910058
Minimum0
Maximum86364.574
Zeros11548
Zeros (%)29.8%
Negative0
Negative (%)0.0%
Memory size604.5 KiB
2024-07-15T21:02:03.928997image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.0502585
Q32.5664872
95-th percentile12.507459
Maximum86364.574
Range86364.574
Interquartile range (IQR)2.5664872

Descriptive statistics

Standard deviation439.40573
Coefficient of variation (CV)68.753768
Kurtosis38567.335
Mean6.3910058
Median Absolute Deviation (MAD)2.04624
Skewness196.23332
Sum247255.23
Variance193077.4
MonotonicityNot monotonic
2024-07-15T21:02:04.134358image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11548
29.8%
0.000153 13
 
< 0.1%
0.000149 12
 
< 0.1%
2.250014 12
 
< 0.1%
2.266504 12
 
< 0.1%
0.000151 12
 
< 0.1%
0.00015 11
 
< 0.1%
2.4 × 10-511
 
< 0.1%
2.299984 11
 
< 0.1%
0.000148 11
 
< 0.1%
Other values (22319) 27035
69.9%
ValueCountFrequency (%)
0 11548
29.8%
4 × 10-61
 
< 0.1%
6 × 10-62
 
< 0.1%
7 × 10-65
 
< 0.1%
8 × 10-63
 
< 0.1%
9 × 10-63
 
< 0.1%
1 × 10-51
 
< 0.1%
1.1 × 10-54
 
< 0.1%
1.2 × 10-52
 
< 0.1%
1.3 × 10-55
 
< 0.1%
ValueCountFrequency (%)
86364.57392 1
< 0.1%
584.893654 1
< 0.1%
584.860786 1
< 0.1%
584.823412 1
< 0.1%
524.387331 1
< 0.1%
481.374873 1
< 0.1%
448.400995 1
< 0.1%
427.426069 1
< 0.1%
422.250216 1
< 0.1%
400.295262 1
< 0.1%

source_bytes
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1131
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean233.77502
Minimum0
Maximum240680
Zeros17698
Zeros (%)45.7%
Negative0
Negative (%)0.0%
Memory size604.5 KiB
2024-07-15T21:02:04.333601image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median64
Q366
95-th percentile1057
Maximum240680
Range240680
Interquartile range (IQR)66

Descriptive statistics

Standard deviation1622.8525
Coefficient of variation (CV)6.9419413
Kurtosis12678.992
Mean233.77502
Median Absolute Deviation (MAD)64
Skewness91.802705
Sum9044288
Variance2633650.2
MonotonicityNot monotonic
2024-07-15T21:02:04.548275image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17698
45.7%
66 11151
28.8%
412 2037
 
5.3%
404 1815
 
4.7%
1057 855
 
2.2%
4 422
 
1.1%
6 353
 
0.9%
48 269
 
0.7%
31 216
 
0.6%
150 163
 
0.4%
Other values (1121) 3709
 
9.6%
ValueCountFrequency (%)
0 17698
45.7%
1 2
 
< 0.1%
4 422
 
1.1%
6 353
 
0.9%
7 56
 
0.1%
8 23
 
0.1%
10 1
 
< 0.1%
16 1
 
< 0.1%
17 1
 
< 0.1%
18 8
 
< 0.1%
ValueCountFrequency (%)
240680 1
< 0.1%
58391 1
< 0.1%
57856 1
< 0.1%
57344 1
< 0.1%
55906 1
< 0.1%
52515 1
< 0.1%
43344 1
< 0.1%
34487 1
< 0.1%
27131 1
< 0.1%
27099 1
< 0.1%

destination_bytes
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct236
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean217.92377
Minimum0
Maximum912771
Zeros29413
Zeros (%)76.0%
Negative0
Negative (%)0.0%
Memory size604.5 KiB
2024-07-15T21:02:04.747726image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile244
Maximum912771
Range912771
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7214.1393
Coefficient of variation (CV)33.103957
Kurtosis7410.5274
Mean217.92377
Median Absolute Deviation (MAD)0
Skewness73.008324
Sum8431035
Variance52043806
MonotonicityNot monotonic
2024-07-15T21:02:04.974003image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29413
76.0%
164 1617
 
4.2%
113 1120
 
2.9%
152 1051
 
2.7%
735 853
 
2.2%
175 643
 
1.7%
66 438
 
1.1%
57 368
 
1.0%
244 363
 
0.9%
48 358
 
0.9%
Other values (226) 2464
 
6.4%
ValueCountFrequency (%)
0 29413
76.0%
4 58
 
0.1%
8 1
 
< 0.1%
9 4
 
< 0.1%
17 10
 
< 0.1%
21 2
 
< 0.1%
22 7
 
< 0.1%
23 21
 
0.1%
24 19
 
< 0.1%
25 13
 
< 0.1%
ValueCountFrequency (%)
912771 1
< 0.1%
366716 1
< 0.1%
327077 1
< 0.1%
317797 1
< 0.1%
316965 1
< 0.1%
278518 1
< 0.1%
276358 1
< 0.1%
254513 1
< 0.1%
235628 1
< 0.1%
222359 1
< 0.1%

count
Real number (ℝ)

ZEROS 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.25180935
Minimum0
Maximum17
Zeros33384
Zeros (%)86.3%
Negative0
Negative (%)0.0%
Memory size604.5 KiB
2024-07-15T21:02:05.134261image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum17
Range17
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.85239166
Coefficient of variation (CV)3.3850676
Kurtosis68.379584
Mean0.25180935
Median Absolute Deviation (MAD)0
Skewness6.4756078
Sum9742
Variance0.72657154
MonotonicityNot monotonic
2024-07-15T21:02:05.302465image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 33384
86.3%
1 3191
 
8.2%
2 1007
 
2.6%
3 617
 
1.6%
4 246
 
0.6%
5 100
 
0.3%
6 62
 
0.2%
7 14
 
< 0.1%
9 13
 
< 0.1%
8 12
 
< 0.1%
Other values (8) 42
 
0.1%
ValueCountFrequency (%)
0 33384
86.3%
1 3191
 
8.2%
2 1007
 
2.6%
3 617
 
1.6%
4 246
 
0.6%
5 100
 
0.3%
6 62
 
0.2%
7 14
 
< 0.1%
8 12
 
< 0.1%
9 13
 
< 0.1%
ValueCountFrequency (%)
17 3
 
< 0.1%
16 2
 
< 0.1%
15 3
 
< 0.1%
14 4
 
< 0.1%
13 5
 
< 0.1%
12 6
< 0.1%
11 8
< 0.1%
10 11
< 0.1%
9 13
< 0.1%
8 12
< 0.1%

same_srv_rate
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0.0
33500 
1.0
5172 
0.5
 
13
0.67
 
2
0.75
 
1

Length

Max length4
Median length3
Mean length3.0000775
Min length3

Characters and Unicode

Total characters116067
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 33500
86.6%
1.0 5172
 
13.4%
0.5 13
 
< 0.1%
0.67 2
 
< 0.1%
0.75 1
 
< 0.1%

Length

2024-07-15T21:02:05.505730image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-15T21:02:05.737170image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 33500
86.6%
1.0 5172
 
13.4%
0.5 13
 
< 0.1%
0.67 2
 
< 0.1%
0.75 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 72188
62.2%
. 38688
33.3%
1 5172
 
4.5%
5 14
 
< 0.1%
7 3
 
< 0.1%
6 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 116067
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 72188
62.2%
. 38688
33.3%
1 5172
 
4.5%
5 14
 
< 0.1%
7 3
 
< 0.1%
6 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 116067
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 72188
62.2%
. 38688
33.3%
1 5172
 
4.5%
5 14
 
< 0.1%
7 3
 
< 0.1%
6 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 116067
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 72188
62.2%
. 38688
33.3%
1 5172
 
4.5%
5 14
 
< 0.1%
7 3
 
< 0.1%
6 2
 
< 0.1%

serror_rate
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0.0
38222 
1.0
 
455
0.5
 
9
0.33
 
2

Length

Max length4
Median length3
Mean length3.0000517
Min length3

Characters and Unicode

Total characters116066
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 38222
98.8%
1.0 455
 
1.2%
0.5 9
 
< 0.1%
0.33 2
 
< 0.1%

Length

2024-07-15T21:02:05.918029image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-15T21:02:06.080878image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 38222
98.8%
1.0 455
 
1.2%
0.5 9
 
< 0.1%
0.33 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 76910
66.3%
. 38688
33.3%
1 455
 
0.4%
5 9
 
< 0.1%
3 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 116066
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 76910
66.3%
. 38688
33.3%
1 455
 
0.4%
5 9
 
< 0.1%
3 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 116066
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 76910
66.3%
. 38688
33.3%
1 455
 
0.4%
5 9
 
< 0.1%
3 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 116066
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 76910
66.3%
. 38688
33.3%
1 455
 
0.4%
5 9
 
< 0.1%
3 4
 
< 0.1%

srv_serror_rate
Real number (ℝ)

ZEROS 

Distinct78
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22394515
Minimum0
Maximum1
Zeros27476
Zeros (%)71.0%
Negative0
Negative (%)0.0%
Memory size604.5 KiB
2024-07-15T21:02:06.275495image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.33
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.38601897
Coefficient of variation (CV)1.7237211
Kurtosis-0.059061155
Mean0.22394515
Median Absolute Deviation (MAD)0
Skewness1.3229399
Sum8663.99
Variance0.14901065
MonotonicityNot monotonic
2024-07-15T21:02:06.501643image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 27476
71.0%
1 5173
 
13.4%
0.5 1379
 
3.6%
0.33 815
 
2.1%
0.25 474
 
1.2%
0.99 445
 
1.2%
0.2 270
 
0.7%
0.98 254
 
0.7%
0.67 235
 
0.6%
0.96 215
 
0.6%
Other values (68) 1952
 
5.0%
ValueCountFrequency (%)
0 27476
71.0%
0.01 22
 
0.1%
0.05 2
 
< 0.1%
0.06 2
 
< 0.1%
0.07 7
 
< 0.1%
0.08 12
 
< 0.1%
0.09 11
 
< 0.1%
0.1 12
 
< 0.1%
0.11 22
 
0.1%
0.12 51
 
0.1%
ValueCountFrequency (%)
1 5173
13.4%
0.99 445
 
1.2%
0.98 254
 
0.7%
0.97 189
 
0.5%
0.96 215
 
0.6%
0.95 131
 
0.3%
0.94 110
 
0.3%
0.93 87
 
0.2%
0.92 74
 
0.2%
0.91 67
 
0.2%

dst_host_count
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.577543
Minimum0
Maximum100
Zeros27648
Zeros (%)71.5%
Negative0
Negative (%)0.0%
Memory size604.5 KiB
2024-07-15T21:02:06.721181image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile86
Maximum100
Range100
Interquartile range (IQR)2

Descriptive statistics

Standard deviation26.35099
Coefficient of variation (CV)2.2760433
Kurtosis3.7036669
Mean11.577543
Median Absolute Deviation (MAD)0
Skewness2.2652358
Sum447912
Variance694.37468
MonotonicityNot monotonic
2024-07-15T21:02:06.942957image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 27648
71.5%
1 1162
 
3.0%
2 686
 
1.8%
100 425
 
1.1%
3 425
 
1.1%
4 347
 
0.9%
16 274
 
0.7%
94 273
 
0.7%
5 272
 
0.7%
6 220
 
0.6%
Other values (91) 6956
 
18.0%
ValueCountFrequency (%)
0 27648
71.5%
1 1162
 
3.0%
2 686
 
1.8%
3 425
 
1.1%
4 347
 
0.9%
5 272
 
0.7%
6 220
 
0.6%
7 183
 
0.5%
8 162
 
0.4%
9 133
 
0.3%
ValueCountFrequency (%)
100 425
1.1%
99 9
 
< 0.1%
98 78
 
0.2%
97 97
 
0.3%
96 38
 
0.1%
95 41
 
0.1%
94 273
0.7%
93 161
 
0.4%
92 119
 
0.3%
91 137
 
0.4%

dst_host_srv_count
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.409843
Minimum0
Maximum100
Zeros22340
Zeros (%)57.7%
Negative0
Negative (%)0.0%
Memory size604.5 KiB
2024-07-15T21:02:07.155645image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q323
95-th percentile93
Maximum100
Range100
Interquartile range (IQR)23

Descriptive statistics

Standard deviation33.859126
Coefficient of variation (CV)1.7444307
Kurtosis0.25422191
Mean19.409843
Median Absolute Deviation (MAD)0
Skewness1.4132313
Sum750928
Variance1146.4404
MonotonicityNot monotonic
2024-07-15T21:02:07.370104image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 22340
57.7%
1 3988
 
10.3%
100 927
 
2.4%
2 569
 
1.5%
83 407
 
1.1%
86 385
 
1.0%
88 354
 
0.9%
87 326
 
0.8%
85 315
 
0.8%
89 300
 
0.8%
Other values (91) 8777
 
22.7%
ValueCountFrequency (%)
0 22340
57.7%
1 3988
 
10.3%
2 569
 
1.5%
3 204
 
0.5%
4 137
 
0.4%
5 129
 
0.3%
6 113
 
0.3%
7 111
 
0.3%
8 96
 
0.2%
9 105
 
0.3%
ValueCountFrequency (%)
100 927
2.4%
99 69
 
0.2%
98 138
 
0.4%
97 213
 
0.6%
96 123
 
0.3%
95 67
 
0.2%
94 295
 
0.8%
93 219
 
0.6%
92 169
 
0.4%
91 212
 
0.5%

dst_host_same_src_port_rate
Real number (ℝ)

ZEROS 

Distinct41
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.049109801
Minimum0
Maximum1
Zeros35478
Zeros (%)91.7%
Negative0
Negative (%)0.0%
Memory size604.5 KiB
2024-07-15T21:02:07.592548image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.25
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.21165457
Coefficient of variation (CV)4.3098233
Kurtosis15.988024
Mean0.049109801
Median Absolute Deviation (MAD)0
Skewness4.2253338
Sum1899.96
Variance0.044797657
MonotonicityNot monotonic
2024-07-15T21:02:07.806957image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 35478
91.7%
1 1798
 
4.6%
0.01 536
 
1.4%
0.02 378
 
1.0%
0.03 93
 
0.2%
0.33 41
 
0.1%
0.2 40
 
0.1%
0.25 37
 
0.1%
0.5 32
 
0.1%
0.05 28
 
0.1%
Other values (31) 227
 
0.6%
ValueCountFrequency (%)
0 35478
91.7%
0.01 536
 
1.4%
0.02 378
 
1.0%
0.03 93
 
0.2%
0.04 28
 
0.1%
0.05 28
 
0.1%
0.06 19
 
< 0.1%
0.07 12
 
< 0.1%
0.08 15
 
< 0.1%
0.09 11
 
< 0.1%
ValueCountFrequency (%)
1 1798
4.6%
0.8 1
 
< 0.1%
0.75 1
 
< 0.1%
0.67 8
 
< 0.1%
0.5 32
 
0.1%
0.46 1
 
< 0.1%
0.44 1
 
< 0.1%
0.42 1
 
< 0.1%
0.4 15
 
< 0.1%
0.39 1
 
< 0.1%

dst_host_serror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.034356131
Minimum0
Maximum1
Zeros37342
Zeros (%)96.5%
Negative0
Negative (%)0.0%
Memory size604.5 KiB
2024-07-15T21:02:08.005073image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.18147467
Coefficient of variation (CV)5.2821626
Kurtosis24.160841
Mean0.034356131
Median Absolute Deviation (MAD)0
Skewness5.1096062
Sum1329.17
Variance0.032933057
MonotonicityNot monotonic
2024-07-15T21:02:08.204167image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 37342
96.5%
1 1305
 
3.4%
0.67 4
 
< 0.1%
0.62 3
 
< 0.1%
0.5 3
 
< 0.1%
0.61 3
 
< 0.1%
0.6 3
 
< 0.1%
0.59 3
 
< 0.1%
0.71 2
 
< 0.1%
0.75 2
 
< 0.1%
Other values (15) 18
 
< 0.1%
ValueCountFrequency (%)
0 37342
96.5%
0.25 1
 
< 0.1%
0.33 1
 
< 0.1%
0.4 1
 
< 0.1%
0.43 1
 
< 0.1%
0.44 1
 
< 0.1%
0.5 3
 
< 0.1%
0.53 1
 
< 0.1%
0.54 1
 
< 0.1%
0.55 1
 
< 0.1%
ValueCountFrequency (%)
1 1305
3.4%
0.8 1
 
< 0.1%
0.75 2
 
< 0.1%
0.73 1
 
< 0.1%
0.71 2
 
< 0.1%
0.7 1
 
< 0.1%
0.69 1
 
< 0.1%
0.67 4
 
< 0.1%
0.62 3
 
< 0.1%
0.61 3
 
< 0.1%

dst_host_srv_serror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.063154983
Minimum0
Maximum1
Zeros36220
Zeros (%)93.6%
Negative0
Negative (%)0.0%
Memory size604.5 KiB
2024-07-15T21:02:08.395409image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.24237129
Coefficient of variation (CV)3.8377224
Kurtosis10.842309
Mean0.063154983
Median Absolute Deviation (MAD)0
Skewness3.5820694
Sum2443.34
Variance0.058743844
MonotonicityNot monotonic
2024-07-15T21:02:08.593919image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 36220
93.6%
1 2188
 
5.7%
0.96 57
 
0.1%
0.99 32
 
0.1%
0.98 32
 
0.1%
0.91 28
 
0.1%
0.97 23
 
0.1%
0.87 17
 
< 0.1%
0.9 14
 
< 0.1%
0.92 12
 
< 0.1%
Other values (13) 65
 
0.2%
ValueCountFrequency (%)
0 36220
93.6%
0.14 2
 
< 0.1%
0.17 2
 
< 0.1%
0.2 2
 
< 0.1%
0.25 2
 
< 0.1%
0.33 3
 
< 0.1%
0.5 1
 
< 0.1%
0.67 1
 
< 0.1%
0.75 1
 
< 0.1%
0.87 17
 
< 0.1%
ValueCountFrequency (%)
1 2188
5.7%
0.99 32
 
0.1%
0.98 32
 
0.1%
0.97 23
 
0.1%
0.96 57
 
0.1%
0.95 12
 
< 0.1%
0.94 10
 
< 0.1%
0.93 10
 
< 0.1%
0.92 12
 
< 0.1%
0.91 28
 
0.1%

label
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
1
35712 
0
 
2976

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters38688
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 35712
92.3%
0 2976
 
7.7%

Length

2024-07-15T21:02:08.774918image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-15T21:02:08.918724image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 35712
92.3%
0 2976
 
7.7%

Most occurring characters

ValueCountFrequency (%)
1 35712
92.3%
0 2976
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38688
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 35712
92.3%
0 2976
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38688
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 35712
92.3%
0 2976
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38688
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 35712
92.3%
0 2976
 
7.7%

source_ip_address
Real number (ℝ)

Distinct2057
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2322457 × 109
Minimum3.2322355 × 109
Maximum3.232301 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size604.5 KiB
2024-07-15T21:02:09.090439image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum3.2322355 × 109
5-th percentile3.2322358 × 109
Q13.2322389 × 109
median3.2322435 × 109
Q33.2322499 × 109
95-th percentile3.2322619 × 109
Maximum3.232301 × 109
Range65518
Interquartile range (IQR)10990

Descriptive statistics

Standard deviation8282.0032
Coefficient of variation (CV)2.5623062 × 10-6
Kurtosis0.48782402
Mean3.2322457 × 109
Median Absolute Deviation (MAD)5668
Skewness0.87700872
Sum1.2504912 × 1014
Variance68591576
MonotonicityNot monotonic
2024-07-15T21:02:09.322284image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3232241425 1341
 
3.5%
3232260483 1015
 
2.6%
3232249861 890
 
2.3%
3232249687 597
 
1.5%
3232238272 576
 
1.5%
3232255202 536
 
1.4%
3232236802 531
 
1.4%
3232239542 467
 
1.2%
3232235749 450
 
1.2%
3232244967 441
 
1.1%
Other values (2047) 31844
82.3%
ValueCountFrequency (%)
3232235530 1
 
< 0.1%
3232235532 90
0.2%
3232235537 3
 
< 0.1%
3232235539 1
 
< 0.1%
3232235541 6
 
< 0.1%
3232235542 1
 
< 0.1%
3232235543 1
 
< 0.1%
3232235546 4
 
< 0.1%
3232235550 1
 
< 0.1%
3232235564 9
 
< 0.1%
ValueCountFrequency (%)
3232301048 1
 
< 0.1%
3232300958 15
< 0.1%
3232268268 2
 
< 0.1%
3232268219 5
 
< 0.1%
3232268156 3
 
< 0.1%
3232268128 1
 
< 0.1%
3232268120 4
 
< 0.1%
3232268116 1
 
< 0.1%
3232268060 1
 
< 0.1%
3232267995 9
< 0.1%

source_port_number
Real number (ℝ)

HIGH CORRELATION 

Distinct6836
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7270.2025
Minimum0
Maximum65415
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size604.5 KiB
2024-07-15T21:02:09.548995image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q18
median1525
Q34023
95-th percentile49419.45
Maximum65415
Range65415
Interquartile range (IQR)4015

Descriptive statistics

Standard deviation15057.935
Coefficient of variation (CV)2.0711852
Kurtosis4.7378978
Mean7270.2025
Median Absolute Deviation (MAD)1517
Skewness2.4462427
Sum2.8126959 × 108
Variance2.2674142 × 108
MonotonicityNot monotonic
2024-07-15T21:02:09.771554image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 14095
36.4%
3 564
 
1.5%
123 473
 
1.2%
445 314
 
0.8%
137 293
 
0.8%
1025 256
 
0.7%
32776 195
 
0.5%
33768 187
 
0.5%
32788 179
 
0.5%
1440 176
 
0.5%
Other values (6826) 21956
56.8%
ValueCountFrequency (%)
0 3
 
< 0.1%
3 564
 
1.5%
5 2
 
< 0.1%
8 14095
36.4%
11 9
 
< 0.1%
50 4
 
< 0.1%
123 473
 
1.2%
137 293
 
0.8%
138 59
 
0.2%
139 160
 
0.4%
ValueCountFrequency (%)
65415 1
 
< 0.1%
65401 1
 
< 0.1%
65253 3
< 0.1%
64991 1
 
< 0.1%
64929 1
 
< 0.1%
64907 3
< 0.1%
64901 1
 
< 0.1%
64765 3
< 0.1%
64672 3
< 0.1%
64644 1
 
< 0.1%

destination_ip_address
Real number (ℝ)

Distinct549
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2322443 × 109
Minimum3.2322355 × 109
Maximum3.2322682 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size604.5 KiB
2024-07-15T21:02:09.975214image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum3.2322355 × 109
5-th percentile3.2322363 × 109
Q13.2322394 × 109
median3.232241 × 109
Q33.2322488 × 109
95-th percentile3.2322626 × 109
Maximum3.2322682 × 109
Range32656
Interquartile range (IQR)9411

Descriptive statistics

Standard deviation8133.4657
Coefficient of variation (CV)2.5163524 × 10-6
Kurtosis0.57167122
Mean3.2322443 × 109
Median Absolute Deviation (MAD)3895
Skewness1.2242637
Sum1.2504907 × 1014
Variance66153264
MonotonicityNot monotonic
2024-07-15T21:02:10.209001image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3232239387 2927
 
7.6%
3232239423 2611
 
6.7%
3232236490 2134
 
5.5%
3232241530 1611
 
4.2%
3232241031 1160
 
3.0%
3232239612 577
 
1.5%
3232241008 395
 
1.0%
3232239518 327
 
0.8%
3232235627 317
 
0.8%
3232236476 243
 
0.6%
Other values (539) 26386
68.2%
ValueCountFrequency (%)
3232235529 2
 
< 0.1%
3232235532 1
 
< 0.1%
3232235539 1
 
< 0.1%
3232235546 1
 
< 0.1%
3232235550 1
 
< 0.1%
3232235564 147
0.4%
3232235580 97
0.3%
3232235589 10
 
< 0.1%
3232235591 1
 
< 0.1%
3232235602 2
 
< 0.1%
ValueCountFrequency (%)
3232268185 6
 
< 0.1%
3232268105 1
 
< 0.1%
3232268064 78
0.2%
3232268013 78
0.2%
3232267793 7
 
< 0.1%
3232267775 2
 
< 0.1%
3232267668 83
0.2%
3232267491 1
 
< 0.1%
3232267291 85
0.2%
3232267126 101
0.3%

destination_port_number
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct220
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1422.4205
Minimum0
Maximum65258
Zeros14104
Zeros (%)36.5%
Negative0
Negative (%)0.0%
Memory size604.5 KiB
2024-07-15T21:02:10.420849image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median123
Q31433
95-th percentile3389
Maximum65258
Range65258
Interquartile range (IQR)1433

Descriptive statistics

Standard deviation5541.4879
Coefficient of variation (CV)3.8958156
Kurtosis78.077007
Mean1422.4205
Median Absolute Deviation (MAD)123
Skewness8.3698961
Sum55030603
Variance30708088
MonotonicityNot monotonic
2024-07-15T21:02:10.620935image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14104
36.5%
139 5967
15.4%
2967 3117
 
8.1%
25 2940
 
7.6%
1434 2933
 
7.6%
1433 2034
 
5.3%
22 785
 
2.0%
445 664
 
1.7%
80 581
 
1.5%
137 568
 
1.5%
Other values (210) 4995
 
12.9%
ValueCountFrequency (%)
0 14104
36.5%
1 55
 
0.1%
3 420
 
1.1%
8 1
 
< 0.1%
10 56
 
0.1%
13 33
 
0.1%
21 9
 
< 0.1%
22 785
 
2.0%
25 2940
 
7.6%
53 17
 
< 0.1%
ValueCountFrequency (%)
65258 1
 
< 0.1%
65120 1
 
< 0.1%
64936 1
 
< 0.1%
64450 1
 
< 0.1%
63324 4
 
< 0.1%
60502 12
 
< 0.1%
59879 144
0.4%
58975 12
 
< 0.1%
58182 1
 
< 0.1%
57610 1
 
< 0.1%

freq_service
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34455.611
Minimum1
Maximum37331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size604.5 KiB
2024-07-15T21:02:10.787121image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2372
Q137331
median37331
Q337331
95-th percentile37331
Maximum37331
Range37330
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9677.757
Coefficient of variation (CV)0.28087608
Kurtosis7.4394538
Mean34455.611
Median Absolute Deviation (MAD)0
Skewness-3.0709287
Sum1.3330187 × 109
Variance93658980
MonotonicityNot monotonic
2024-07-15T21:02:10.937082image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
37331 35548
91.9%
2372 2372
 
6.1%
577 572
 
1.5%
135 135
 
0.3%
30 30
 
0.1%
27 27
 
0.1%
3 3
 
< 0.1%
1 1
 
< 0.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
3 3
 
< 0.1%
27 27
 
0.1%
30 30
 
0.1%
135 135
 
0.3%
577 572
 
1.5%
2372 2372
 
6.1%
37331 35548
91.9%
ValueCountFrequency (%)
37331 35548
91.9%
2372 2372
 
6.1%
577 572
 
1.5%
135 135
 
0.3%
30 30
 
0.1%
27 27
 
0.1%
3 3
 
< 0.1%
1 1
 
< 0.1%

freq_flag
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11464.603
Minimum1
Maximum16682
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size604.5 KiB
2024-07-15T21:02:11.085351image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2040
Q15643
median12040
Q316682
95-th percentile16682
Maximum16682
Range16681
Interquartile range (IQR)11039

Descriptive statistics

Standard deviation5349.5257
Coefficient of variation (CV)0.46661238
Kurtosis-1.1046006
Mean11464.603
Median Absolute Deviation (MAD)4642
Skewness-0.5798468
Sum4.4354256 × 108
Variance28617425
MonotonicityNot monotonic
2024-07-15T21:02:11.242152image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
16682 15441
39.9%
12040 11518
29.8%
5643 5643
 
14.6%
3342 3332
 
8.6%
2040 2040
 
5.3%
306 294
 
0.8%
172 172
 
0.4%
109 107
 
0.3%
76 75
 
0.2%
32 32
 
0.1%
Other values (3) 34
 
0.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
15 15
 
< 0.1%
18 18
 
< 0.1%
32 32
 
0.1%
76 75
 
0.2%
109 107
 
0.3%
172 172
 
0.4%
306 294
 
0.8%
2040 2040
5.3%
3342 3332
8.6%
ValueCountFrequency (%)
16682 15441
39.9%
12040 11518
29.8%
5643 5643
 
14.6%
3342 3332
 
8.6%
2040 2040
 
5.3%
306 294
 
0.8%
172 172
 
0.4%
109 107
 
0.3%
76 75
 
0.2%
32 32
 
0.1%

freq_ashula
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34559.706
Minimum1
Maximum37348
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size604.5 KiB
2024-07-15T21:02:11.388730image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2941
Q137348
median37348
Q337348
95-th percentile37348
Maximum37348
Range37347
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9416.3775
Coefficient of variation (CV)0.27246694
Kurtosis7.5093523
Mean34559.706
Median Absolute Deviation (MAD)0
Skewness-3.082653
Sum1.3370459 × 109
Variance88668164
MonotonicityNot monotonic
2024-07-15T21:02:11.546079image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
37348 35568
91.9%
2941 2933
 
7.6%
161 161
 
0.4%
19 19
 
< 0.1%
4 4
 
< 0.1%
2 2
 
< 0.1%
1 1
 
< 0.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 2
 
< 0.1%
4 4
 
< 0.1%
19 19
 
< 0.1%
161 161
 
0.4%
2941 2933
 
7.6%
37348 35568
91.9%
ValueCountFrequency (%)
37348 35568
91.9%
2941 2933
 
7.6%
161 161
 
0.4%
19 19
 
< 0.1%
4 4
 
< 0.1%
2 2
 
< 0.1%
1 1
 
< 0.1%

freq_protocol
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
19107
18750 
15847
14669 
5683
5269 

Length

Max length5
Median length5
Mean length4.8638079
Min length4

Characters and Unicode

Total characters188171
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5683
2nd row5683
3rd row5683
4th row5683
5th row19107

Common Values

ValueCountFrequency (%)
19107 18750
48.5%
15847 14669
37.9%
5683 5269
 
13.6%

Length

2024-07-15T21:02:12.178414image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-15T21:02:12.331689image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
19107 18750
48.5%
15847 14669
37.9%
5683 5269
 
13.6%

Most occurring characters

ValueCountFrequency (%)
1 52169
27.7%
7 33419
17.8%
5 19938
 
10.6%
8 19938
 
10.6%
9 18750
 
10.0%
0 18750
 
10.0%
4 14669
 
7.8%
6 5269
 
2.8%
3 5269
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 188171
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 52169
27.7%
7 33419
17.8%
5 19938
 
10.6%
8 19938
 
10.6%
9 18750
 
10.0%
0 18750
 
10.0%
4 14669
 
7.8%
6 5269
 
2.8%
3 5269
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 188171
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 52169
27.7%
7 33419
17.8%
5 19938
 
10.6%
8 19938
 
10.6%
9 18750
 
10.0%
0 18750
 
10.0%
4 14669
 
7.8%
6 5269
 
2.8%
3 5269
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 188171
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 52169
27.7%
7 33419
17.8%
5 19938
 
10.6%
8 19938
 
10.6%
9 18750
 
10.0%
0 18750
 
10.0%
4 14669
 
7.8%
6 5269
 
2.8%
3 5269
 
2.8%

Interactions

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2024-07-15T21:01:37.073586image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:39.834113image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:42.361112image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:44.992315image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:47.971356image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:50.650727image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:53.294443image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:55.994295image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:59.083238image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:02:01.916642image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:18.972290image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:21.594094image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:24.108774image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:26.884142image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:29.436432image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:32.173026image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:34.598011image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:37.234828image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:39.988527image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:42.519097image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:45.157042image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:48.134258image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:50.814539image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:53.461329image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:56.161637image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:59.252956image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:02:02.070052image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:19.123547image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:21.743254image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:24.256484image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:27.062465image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:29.582580image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:32.317155image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:34.741991image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:37.379416image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:40.142250image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:42.669711image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:45.325358image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:48.290522image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:50.976354image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:53.620627image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:56.326863image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:59.429636image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:02:02.239555image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:19.278075image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:21.890880image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:24.405766image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:27.225555image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:29.731363image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:32.463675image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:34.894751image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:37.536899image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:40.295625image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:42.822954image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:45.497539image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:48.459367image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:51.143158image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:53.781420image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:56.495490image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:59.593047image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:02:02.395036image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:19.422757image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:22.042690image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:24.552086image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:27.386170image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:29.876353image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:32.608838image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:35.053209image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:37.693257image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:40.458683image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:42.979764image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:45.663909image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:48.625216image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:51.301850image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:53.945168image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:56.660315image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:59.743305image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:02:02.546365image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:19.576618image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:22.194392image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:24.706650image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:27.549310image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:30.032821image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:32.754133image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:35.199705image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:38.082584image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:40.608951image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:43.146449image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:45.844701image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:48.787890image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:51.469513image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:54.121531image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:56.820606image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:59.909635image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:02:02.698875image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:19.726700image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:22.343488image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:25.018679image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:27.712456image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:30.192911image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:32.908825image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:35.363027image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:38.233452image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:40.759120image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:43.305382image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:46.014393image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:48.942029image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:51.627175image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:54.285184image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:01:56.983091image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-15T21:02:00.073221image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2024-07-15T21:02:12.477213image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
countdestination_bytesdestination_ip_addressdestination_port_numberdst_host_countdst_host_same_src_port_ratedst_host_serror_ratedst_host_srv_countdst_host_srv_serror_ratedurationfreq_ashulafreq_flagfreq_protocolfreq_servicelabelsame_srv_rateserror_ratesource_bytessource_ip_addresssource_port_numbersrv_serror_rate
count1.0000.304-0.1660.1800.4810.204-0.0230.402-0.047-0.0770.095-0.4290.159-0.0480.0350.3070.0450.117-0.0630.300-0.055
destination_bytes0.3041.000-0.3420.0500.604-0.071-0.1050.615-0.1450.4040.114-0.5650.015-0.4160.0150.0000.0000.692-0.0090.259-0.214
destination_ip_address-0.166-0.3421.000-0.038-0.305-0.080-0.140-0.320-0.084-0.112-0.0810.2840.2440.1960.3810.1190.022-0.2050.054-0.1700.074
destination_port_number0.1800.050-0.0381.0000.2690.1800.1600.1760.240-0.161-0.320-0.6180.1290.0500.0550.0150.021-0.3870.0030.7300.301
dst_host_count0.4810.604-0.3050.2691.0000.4560.2680.8280.1280.2100.151-0.6430.317-0.1270.1310.2220.0240.365-0.0780.411-0.162
dst_host_same_src_port_rate0.204-0.071-0.0800.1800.4561.0000.4860.3720.332-0.1900.089-0.2620.196-0.0020.1060.0400.067-0.160-0.0550.193-0.023
dst_host_serror_rate-0.023-0.105-0.1400.1600.2680.4861.0000.2380.724-0.0950.056-0.0370.143-0.0760.0530.0000.087-0.105-0.0930.1250.020
dst_host_srv_count0.4020.615-0.3200.1760.8280.3720.2381.0000.2470.2410.101-0.6160.403-0.3220.5300.2010.0620.375-0.0760.354-0.240
dst_host_srv_serror_rate-0.047-0.145-0.0840.2400.1280.3320.7240.2471.000-0.194-0.254-0.0500.281-0.0230.0740.0260.076-0.197-0.0810.1350.006
duration-0.0770.404-0.112-0.1610.210-0.190-0.0950.241-0.1941.0000.328-0.1220.011-0.2700.0000.0000.0000.607-0.009-0.022-0.131
freq_ashula0.0950.114-0.081-0.3200.1510.0890.0560.101-0.2540.3281.0000.0680.694-0.0880.0850.0950.0230.243-0.002-0.0710.091
freq_flag-0.429-0.5650.284-0.618-0.643-0.262-0.037-0.616-0.050-0.1220.0681.0000.7510.3480.7750.2880.091-0.0760.020-0.769-0.040
freq_protocol0.1590.0150.2440.1290.3170.1960.1430.4030.2810.0110.6940.7511.0000.2350.2610.2210.1660.0140.0590.2750.196
freq_service-0.048-0.4160.1960.050-0.127-0.002-0.076-0.322-0.023-0.270-0.0880.3480.2351.0000.7630.0730.030-0.292-0.012-0.1460.119
label0.0350.0150.3810.0550.1310.1060.0530.5300.0740.0000.0850.7750.2610.7631.0000.0760.0270.0460.0480.0610.158
same_srv_rate0.3070.0000.1190.0150.2220.0400.0000.2010.0260.0000.0950.2880.2210.0730.0761.0000.1610.0000.0480.1120.094
serror_rate0.0450.0000.0220.0210.0240.0670.0870.0620.0760.0000.0230.0910.1660.0300.0270.1611.0000.0000.0350.1910.133
source_bytes0.1170.692-0.205-0.3870.365-0.160-0.1050.375-0.1970.6070.243-0.0760.014-0.2920.0460.0000.0001.000-0.036-0.206-0.338
source_ip_address-0.063-0.0090.0540.003-0.078-0.055-0.093-0.076-0.081-0.009-0.0020.0200.059-0.0120.0480.0480.035-0.0361.000-0.0160.038
source_port_number0.3000.259-0.1700.7300.4110.1930.1250.3540.135-0.022-0.071-0.7690.275-0.1460.0610.1120.191-0.206-0.0161.0000.267
srv_serror_rate-0.055-0.2140.0740.301-0.162-0.0230.020-0.2400.006-0.1310.091-0.0400.1960.1190.1580.0940.133-0.3380.0380.2671.000

Missing values

2024-07-15T21:02:02.957029image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-15T21:02:03.450126image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

durationsource_bytesdestination_bytescountsame_srv_rateserror_ratesrv_serror_ratedst_host_countdst_host_srv_countdst_host_same_src_port_ratedst_host_serror_ratedst_host_srv_serror_ratelabelsource_ip_addresssource_port_numberdestination_ip_addressdestination_port_numberfreq_servicefreq_flagfreq_ashulafreq_protocol
086364.573924240680000.00.00.0000.00.00.01323224576832770323224649186493733112040373485683
10.0000000000.00.00.0000.00.00.013232244597105032322531421434373311204029415683
20.003340484800.00.00.0000.00.00.0132322510721233232241008123373313342373485683
30.0000000000.00.00.0000.00.00.01323223942313832322355641383733112040373485683
40.3117970000.00.00.0000.00.00.0132322455701181032322424871393733156433734819107
51.66712040414400.00.00.0000.00.00.0132322455701181632322424871393733156433734819107
61.65500739322100.00.00.0000.00.00.0132322455701184232322424871393733156433734819107
72.9740890000.00.00.0000.00.00.01323224557011901323223562913937331120403734819107
80.0000000000.00.00.0000.00.00.013232265281393132322438971434373311204029415683
90.0000000000.00.00.0000.00.00.013232257161111932322365411434373311204029415683
durationsource_bytesdestination_bytescountsame_srv_rateserror_ratesrv_serror_ratedst_host_countdst_host_srv_countdst_host_same_src_port_ratedst_host_serror_ratedst_host_srv_serror_ratelabelsource_ip_addresssource_port_numberdestination_ip_addressdestination_port_numberfreq_servicefreq_flagfreq_ashulafreq_protocol
404650.0037580011.00.00.0087870.010.00.01323224142539717323224153014333733120403734819107
404660.0016570021.00.00.0087870.020.00.01323224142539717323224153014333733120403734819107
404670.0055240031.00.00.0087870.000.00.01323224142540035323224153014333733120403734819107
404680.0000000000.00.00.00000.000.00.01323223945183232238565037331166823734815847
404690.0007830031.00.00.0088880.010.00.01323224142540035323224153014333733120403734819107
404700.0020230031.00.00.0088880.000.00.01323224142540351323224153014333733120403734819107
404710.0041440041.00.00.0088880.020.00.01323224142540035323224153014333733120403734819107
404720.0026470031.00.00.0088880.010.00.01323224142540351323224153014333733120403734819107
404740.0031450031.00.00.2588880.000.00.01323224142540569323224153014333733120403734819107
404750.0066450041.00.00.2088880.020.00.01323224142540351323224153014333733120403734819107